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ACEEE Int. J. on Electrical and Power Engineering, Vol. 02, No. 01, Feb 2011 



Design of UPQC with Minimization of DC Link 
voltage for the Improvement of Power Quality by 

Fuzzy Logic Controller 



Mr.R.V.D.Rama Rao 

Assoc. Professir in EEE Dept 

Narasaropeta Engineering Collgee 

Narasaropet, Guntur Dt, Andhra Pradesh, INDIA 

ramrvd@yahoo.com 

Dr.Subhransu Sekhar Dash 

Professor , HOD,EEE, 

SRM University, Chennai, Tamilnadu, INDIA 

munu_dash_2k@yahoo.com 



Abstract — Devices such as power electronics converters, inject 
harmonic currents in the AC system and increase overall 
reactive power demanded by the equivalent loads are presents 
non-linear characteristics. Also, the number of sensitive loads 
that require ideal sinusoidal supply voltages for their proper 
operation has increased. In order to keep power quality under 
limits proposed by standards, it is necessary to include some 
sort of compensation. The aim of this paper is to present a 
unified power quality conditioner (UPQC) with minimization 
of DC Link voltage for the improvement of power quality by 
Fuzzy logic controller as compared with PI controller. By the 
proposed system is comprised of series and shunt Inverters 
which can compensate the sag, swell, unbalance voltage, 
Harmonics and reactive power. PI and fuzzy logic controllers 
are used to stabilize DC link voltage and balance the active 
power between shunt and series inverters for the enhancement 
of power quality. 

Keywords — unified power quality conditioner,fuzzy logic 
controller, powewr quality, dc link voltage 

I.Introduction 

In recent years, the electrical power quality is a more 
and more discussed issue. The main problems are stationary 
and transient distortions in the line voltage such as harmonics, 
flicker, swells and sags and voltage asymmetries. With the 
significant development of power electronics technology, 
especially static power converters [1] (well known as non- 
linear loads), voltage harmonics resulting from current 
harmonics produced by the non-linear loads have become a 
serious problem. Paradoxically, static power converters , the 
source of most of the perturbations, could also be used 
efficiently as active power filters in order to cancel or mitigate 
the above mentioned power quality problems as well as other 
power systems troubles such as damping of voltage 
oscillations . 

The basic principle of active power filtering is to 
synthesize and apply a certain current or voltage waveform 
at a specified point of a distribution network. Active filters 
are fundamentally static power converters designed to 



synthesize a current or voltage source; alternatively, 
magnitude and phase, they can be made to emulate specified 
impedances, both in magnitude and phase[2]. The common 
application of active filtering combines the tasks of harmonic 
filtering and power factor compensation. Apart from this 
complex comprehensive active filters have been proposed 
in the context of total power quality management concept. 
Some of the power filtering applications is categorized as 
custom power devices. The controlling structure, back to 
back inverters might have different operations in 
compensation. For example, they can operate as shunt and 
series active filters to simultaneously compensate the load 
current, harmonics and voltage oscillations. This is called 
unified power quality conditioner; its principle structure is 
given in fieure. ri-31. 



<S) — ____r 



nm^ 



fri^ 






PCC 



Series APF 



ShuntAPF 



Figure 1. Basic structure of UPQC 

UPQC controller provides the compensation 
voltage (v* f ) through the UPQC series inverter and provides 
conditioning current (i* f ) through the generated to be applied 
to series voltage source inverter switches. 

This paper proposes a control technique for UPQCs 
based on a Fuzzy logic controller approach. The proposed 
method operates for allowing the selective compensation of 
voltage and current harmonics with fast dynamical responses. 
Moreover, the impact of dips and over-voltages can be 
attenuated by applying the proposed controller by minimizing 
the DC link voltage distortions. 



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AFUNCTIONAL STRUCTURE OF UPQC 




FT 
\ T 



J Jj* 



^ m, Nonlinear 
L, load 



PAP 

Coimol 



"V, 



Figure 2. Functional structure of UPQC 

The basic functionalities of a UPQC controller are 
depicted in figure 2. The voltage compensation (v*f) and 
current injection (i*f) reference signals, required for com- 
pensation purposes, are evaluated from the instantaneous 
measurements of the source voltage (VS), the dc-bus volt- 
age (Vdc) and the load current (IL). These reference signals 
are compared to the measured feedback signals vl and i2 
and applied to the decoupled voltage and current control- 
lers, which ensure that the compensation signals correspond 
to the reference ones. The gate signals of the power convert- 
ers are obtained by applying pulse width modulators to the 
controlleroutputs. The power converters switch at high fre- 
quency generating a PWM output voltage waveform which 
must be low-pass filtered (in case of series APF and the 
shunt APF). Different approaches have been proposed for 
current control of grid-connected voltage source convert- 
ers. Hysteresis controllers are implemented by means of 
simple analog circuits but, as drawback, the spectrum of the 
output current is not localized which complicates the output 
filter design [10]. PI controllers have been widely applied 
but, due to their finite gain at the fundamental grid frequency, 
they can introduce steady state errors. This can be solved by 
means of Fuzzy logic controller has been also proposed as 
current controllers. 

This study is consisted of three main parts: selection 
of controlling method, design of Active filters, and design 
of Fuzzy logic controller. The design of Active filters section 
has three subsections: shunt active filter control, DC link 
control and series active filter control. Theses parts have 
discussed and finally the simulation results have been 
introduced. 

III.SELECTION OF CONTROLLING METHOD 

UPQC is vastly studied by several researches as an 
infinite method for power quality conditioning [4-6]. 
Different UPQC controlling methods can be classified in 
three following classes: time-domain Abbreviations and 
Acronyms controlling method, frequency-domain controlling 
method and new techniques. Furrier method is one of the 
methods can be named as frequency-domain methods. The 
methods such as P-Q theory, instantaneous reactive power, 
algorithms based on the synchronous d-q reference frame, 



instantaneous power balance method, balanced energy 
method, synchronous detection algorithm, direct detection 
algorithm and notch filter based controlling method are some 
can be mentioned for time-domain methods. Dead beat 
control, space vector modulation and wavelet conversion are 
some of the new techniques [7] . 

Three general standards considered to select the 
controlling method are load characteristics, required accuracy 
and application facility. All methods end in to similar results 
when the reference signal is calculated under balanced and 
sinusoidal conditions where each ends in to different results 
under unbalanced and non sinusoidal conditions. Dead beat 
controlling method presents the best operation among the 
others but more expense should be paid for its calculations. 
Among the introduced methods the reference frame 
methods seem to be more appropriate. The fact is that it needs 
sinusoidal and balanced voltage and is not sensitive to voltage 
distortions and is relatively simple. In result, the response 
time of the control system shortens. So it's prior to utilize 
the synchronous reference frame theory with Fuzzy logic 
controller in UPQC controlling circuit. 

IV.DESIGN OF ACTIVE FILTERS 

For UPQC two different Active filters with proper control 
circuits are provided those are 

a. Series Active Filter (SAF) Control 

b. Shunt Active Filter (PAF) Control 

and one more control also provided for the enhancement of 
system performance. 

c. DC Link voltage control 

A. Series Active Filter (SAF) Control 

Sinusoidal voltage controlling strategy of load is generally 
proposed to control the series part of UPQC. Here, the series 
part of UPQC is controlled in a way that it compensates the 
whole voltage distortions and maintains load voltage 3 -phase 
balanced sinusoidal. In order to reach this, the synchronous 
reference frame theory is applied [8]. 







^ 




v> 


r-Ik 


A. 


a-b< 


V* 


1 




i 


v„-> 


d-q-0 




4*0 




toe' 




a n 




COB 6 






v^+ 




itflO 




Vtf* 


FIL 


W30 


v*+ 








Figure 3. controlling circuit of Series Active Filter 



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In this method the desired value of load phase voltage in d 
axis and q axis is compared with the load voltage and the 
result is considered as the reference signal. The controlling 
circuit of series active filter is shown in Figure. 3. SPWM 
method is used to optimize the response of series inverter. 

B. Shunt Active Filter (PAF) Control 

The measured currents of load are transferred into dqO frame 
using sinusoidal functions through dqO synchronous 
reference frame conversion. The sinusoidal functions are 
obtained through the grid voltage using PLL. Here, the 
currents are divided into AC and DC components. 



~~ hd + hd ' ha ~ l la + he 



(1) 



l id ~ l id ' Hd ' l lq ~ v lq ' *lq 

The active part of current is i and i is the reactive one. AC 
and DC elements can be derived by a low pass filter. 
Controlling algorithm corrects the system's power factor and 
compensates the all current harmonic components by 
generating the reference current as 

**# = hdfte = h q ( 2 ) 

Hear, system's current are: 

hd =ilq> i sq =0 (3) 

Switching losses and the power received from the DC link 
capacitors through the series inverter can decrease the 
average value of DC bus voltage. Other distortions such as 
unbalance conditions and sudden changes in load current 
can result in oscillations in DC bus voltage. 

In order to track the error between the measured and 
desired capacitor voltage values, a PI and Fuzzy logic 
controllers are applied. The resulted controlling signal is 
applied to current control system in shunt voltage source 
inverter which stabilizes the DC capacitor voltage by 
receiving required power from the source. Ai dc , the output of 
PI and Fuzzy logic controllers is added individually to the q 
component of reference current and so the reference current 



Uk 




Figure 4. Controlling circuit of Shunt Active Filter 



I cd 



+ A Wcq 



'Id, ' ^dcScq ~ l lq ( 4 ) 

The reference current is transferred into abc frame 
through reverse conversion of synchronous reference frame 
as shown in Figure. 4,. Resulted reference current (i* , i* 

© ' v fa' fb 

and i* ) are compared with the output current of shunt in- 
verter (i. , L and L ) in PWM. Now, the current controller 

v fa' fb fc 7 ' 



and the required controlling pulses are generated. Required 
compensation current is generated by inverter applying these 
signals to shunt inverter's power switch gates 

DC Link voltage control 

A PI controller is used to track the error exists between the 
measured and desired values of capacitor voltage in order 
to control the DC link voltage as Figure 5 as mentioned in 
[7]. 



Figure 1 . Block diagram of Dc link voltage control 
This signal is applied to current control system in 
shunt voltage source inverter in a way that the DC capacitor 
voltage is stabilized by receiving the required active power 
from the grid. Correct regulation of proportional controller's 
parameter responding speed of control system. Integral gain 
of controller corrects the steady state error of the voltage 
control system. If this gain value is selected large, the re- 
sulted error in steady state is corrected faster and too much 
increase in its value ends in overshoot in system response, 
plays an important role in DC voltage control system's re- 
sponse. Too much increase in proportional gain leads to in- 
stability in control system and too much reduction decreases 
the responding speed of control system. Integral gain of con- 
troller corrects the steady state error of the voltage control 
system. If this gain value is selected large, the resulted error 
in steady state is corrected faster and too much increase in 
its value ends in overshoot in system response. 

IV.Fuzzy Logic Control 

A. Fuzzy logic principle 

The structure of a complete fuzzy control system is 
composed from the following blocs: Fuzzification, 
Knowledgebase, Inference engine, Defuzzification. Figure 
6 shows the structure of a fuzzy logic controller 



X 

*'■ 




RU" 






















V 








Rule Base 


\ 


» 


Purification 




i 










Inference 




L>d unification 










h 




T 


/ 










Date Mm 


/ 



Figure 6. The structure of a fuzzy logic controller 

The fuzzification module converts the crisp values of the 
control inputs into fuzzy values. A fuzzy variable has values 
which are defined by linguistic variables (fuzzy sets or 
subsets) such as low, Medium, high, big, slow . . .where 



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ACEEE Int. J. on Electrical and Power Engineering, Vol. 02, No. 01, Feb 2011 



each is defined by a gradually varying membership function. 
In fuzzy set terminology, all the possible values that a variable 
can assume are named universe of dis course, and the fuzzy 
sets (characterized by membership function) cover the whole 
universe of discourse. The shape fuzzy sets can be triangular, 
trapezoidale, etc [11, 12]. 

A fuzzy control essentially embeds the intuition and 
experience of a human operator, and sometimes those of a 
designer and researcher. The data base and the rules form 
the knowledge base which is used to obtain the inference 
relation R. The data base contains a description of input and 
output variables using fuzzy sets. The rule base is essentially 
the control strategy of the system. It is usually obtained from 
expert knowledge or heuristics, it contains a collection of 
fuzzy conditional statements expressed as a set of IF-THEN 
rules, such as: 

R (i) : If x 1 is F 1 and x 2 is F 2 x n is Fn 

THEN Y is G (i) , i = 1 m" (5) 

Where: (x r x 2 , x n ) is the input variables vector, Yis 

the control variable, M is the number of rules, n is the number 
fuzzy variables, (Fl, F2, . . .Fn) are the fuzzy sets. 

For the given rule base of a control system, the fuzzy 
controller determines the rule base to be fired for the specific 
input signal condition and then computes the effective control 
action (the output fuzzy variable) [11, 13]. The composition 
operation is the method by which such a control output can 
be generated using the rule base. Several composition 
methods, such as max-min or sup-min and max-dot have 
been proposed in the literature. 

The mathematical procedure of converting fuzzy 
values into crisp values is known as 'defuzzification'. A 
number of defuzzification methods have been suggested. The 
choice of defuzzification methods usually depends on the 
application and the available processing power. This 
operation can be performed by several methods of which 
center of gravity (or centroid) and height methods are 
common [13, 14]. 

B. Fuzzy logic controller 

The general structure of a complete fuzzy control system 
is given in Figure. 7. The plant control 'u' is inferred from 
the two state variables, error (e) and change in error (Ae) 
[15]. 



r^ 



■V 



Evaluate 

error and 

changpin 

i-riiir 



Fii/zilkation 






* Inference — > Dek/ilicaiion 



Dau Base 



m 



Planl — ' 



elaboration of this controller is based on the phase plan. The 
control rules are designed to assign a fuzzy set of the con- 
trol input u for each combination of fuzzy sets of e and _e 
[19, 20]. Here NB is negative big, NM is negative medium, 
ZR is zero, PM is positive medium and PB is positive big, 
are labels of fuzzy sets and their corresponding membership 
functions are depicted in Figures. 8, 9 and 10, 
respectively. Table 1 shows one of possible control rule base. 
The rows represent the rate of the error change ey and the 
columns represent the error e. Each pair (e, ey) determines 
the output level NB to PB corresponding to u. 




— a t —a- 



a, „ a 7 



| e~ a 2_e U \_e "2_e 



Figure 8. Membership functions for input I 



NM Z PM PB 




Figure 9 Membership functions for input Ai 




1 Ae 



Figure 10. Membership functions for output u 

The continuity of input membership functions, reasoning 
method, and defuzzification method for the continuity of 
the mapping u fuzz (e, ey) is necessary. In this paper, the 
triangular membership function, the max-min reasoning 
method, and the center of gravity defuzzification method 
are used, as those methods are most frequently used in many 
literatures [15, 18]. 



TABLE I. 



Rules Base for cxxelrent control 



Figure 7. Basic structure of fuzzy control system 

The actual crisp input are approximates to the closer 
values of the respective universes of is course. Hence, the 
fuzzy fied inputs are described by singleton fuzzy sets. The 

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T^j 




^i 


J_V ei 


NB 


NM 


ZR 


PM 


PB 


i 


NB 


NB 


NB 


NM 


NM 


ZR 


NM 


NB 


NM 


NM 


ZR 


PM 


ZR 


NM 


NM 


ZR 


PM 


PM 


PM 


NM 


ZR 


PM 


PM 


GP 


PB 


ZR 


PM 


PM 


GP 


GP 



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ACEEE Int. J. on Electrical and Power Engineering, Vol. 02, No. 01, Feb 2011 







O^^JJ^MATLAB y gjggggfe diaeram d£ UPQC 



V. Simulation and Results 



Ncn Linear Load Cunent 



In order to validate the control strategies as 
discussed above, digital simulation studies were made the 
system described in Figure. 2. The voltage and currents loops 
of the system were also designed and simulated respectively 
with fuzzy control and PI control. The feedback control 
algorithms were iterated until best simulation results were 
obtained. The simulation is realized using the SIMULINK 
software in MATLAB environment simulated circuit is given 
in figure. 11. 

The control presents the best performances, to 
achieve tracking of the desired trajectory. The fuzzy 
controller rejects the load disturbance rapidly with no 
overshoot and with a negligible steady state error. The current 
is limited in its maximal admissible value by a saturation 
function. The reason for superior performance of fuzzy 
controlled system is that basically it is adaptive in nature 
and the controller is able to realize different control law for 
each input state (Error and Change in Error). In this study, 
power circuit is modeled as a 3 -phase 3 -wire system with a 
non linear load comprised of RC load which is connected to 
grid through 3 -phase diode bridge. Circuit parameters used 
in simulation are shown in Table 1 . The simulated load is a 
parallel RC and diode rectifier bridge nonlinear 3-phase load 
which imposes a non sinusoidal current to source. Non-linear 
Load current is shown in Figure 12. 

©2011 ACEEE 40 

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0.03 0.04 0.06 0.03 0.1 0.12 0.14 0.1G 
Time in Sees 

Figure 12. Non-linear Load current 

In Figure. 13 the source current, injected current compensated 
current before and after being compensated by shunt inverter 
are shown. Shunt inverter is activated in 0.02 sec of operation. 
Immediately, the source current is corrected. The results shown 
in Figure. 1 3 present that the shunt part has been able to correct 
the source current appropriately. 

Figure. 14 shows the source side voltage, load side voltage 
and the voltage injected by the series inverter to simulate 
swell and sag of the voltage. As shown in Figure. 14 the voltage 
distortions imposed to load from the source are properly 
compensated by series inverter. In this simulation, series 
inverter operates at 0.02 sec and voltage source faces with 100 
V voltage sag. A voltage swell with 50 V voltage peak occurs 
in 0.08 sec. Simulation results show that the load voltage is 
constant during the operation of UPQC series inverter. 



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ACEEE Int. J. on Electrical and Power Engineering, Vol. 02, No. 01, Feb 2011 



Ipjwtri Voltage 




COG 0.03 0.1 

Time in Sees 

Injected Currefll 




0.06 003 0.1 

Time in Sec? 

C Compensated Cutrent 



0.12 0.14 0.16 




0.04 0.06 0.08 0,1 

Time in Sees 



0.12 0.14 



L1G 



Figure 13. Source current (A), Injected current (B)and compensated 
current(C). 

By using PI and Fuzzy logic Controllers individu- 
ally for shunt controller DC link voltage is shown in Figure. 
15. From that the distortions in DC link voltage can be mini- 
mized by Fuzzy logic controller In this simulation, series 
and shunt inverters start to operate at 0.02 sec. As it is seen, 
capacitor voltage is decreasing until this moment. By oper- 
ating shunt inverter, the capacitor voltage increases and 
reaches to the reference value (600 V). At 0.04 sec of opera- 
tion voltage sag with 100 V amplitude occurs in source volt- 
age. 





> 

c 

1' 

> 






A 


Source Voltage 


Kill 

AM/ 

nil) 


WW- 

i i i i 



0,12 0,14 



Time in Sees 




Figure 14. source side voltage (A), load side voltage (B) and the voltage 
injected (C) 



The average value of capacitor voltage drops about 
10 V occurring this voltage sag and faces with small oscilla- 
tions in lower values. At 0.08 sec of operation voltage swell 
with about 50 Vamplitude occurs at 0.08 sec of operation. 
The average value of capacitor increases about 15 V occur- 
ring this swell and faces with small oscillations in voltages 
around 600 V. Figure 15 shows the exact operation of control 
loop of DC link capacitor voltage from fuzzy logic controller 
settling time (0.04 s) is less than PI controller(0.058s). RL load 
with 6 kW active power and 6 kVAR reactive power is applied 
in simulation to study how reactive power is compensated by 
shunt inverter. Simulation results show that the phase differ- 
ence between voltage and current is cleared by shunt inverter 
operation. Actually, by operating UPQC, required reactive 
power is provided via., UPQC. 





Figure 15. DC link voltage by PI and Fuzzy logic Controller 



Load Power feci or 




J 


0.02 


L J4 


0.06 0,08 0.1 
Loao Hcweiactor 


]12 


0,14 


- 400 

h 












i 

A A 


I 

A 


J»\ A A 


A 


A " 


S 


^Vwi 


wW 


<p\JXJX 


JX 


JX - 


£ -2D0 


. v 


\H 


JVXT\ 


J\ 


f\ 




> 

■d jm 


i 


i 


i i i 


i 


i 



002 



LU-i 



i: 05 



0.03 
t:ms 



V 



0.12 



0.14 



l.'b 



Figure 16. Load Power factor by PI and Fuzzy logic controller 

16 shows the load current and voltage with PI and Fuzzy 
logic controller for simulink circuit simulated individually. 
As it is shown, load current phase leads voltage phase initially. 
At 0.06 sec of operation and operating shunt inverter the phase 
difference between voltage and current gets zero. 

Comparison of this study results with related studies, indicates 
that the proposed system compensates voltage and current 
distortions accurately and the response time of the control 
system is relatively low and also the proposed control system 
is simply applicable. 



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Table II. Simuunk Parameters 



Parameters 


Values 


Source Phase voltage 


220v/50Hz 


DC Link Voltage 


600v 


Shunt inverter rating 


15kVA 


Series inverter rating 


15kVA 


Shunt inverter inductance(Lf) 


3mH 


Shunt inverter Capacitance (Cj) 


IO^iF 


Switching Frequency 


20kHz 


Series inverter inductance(L s ) 


3mH 


Series inverter Capacitance (Cs) 


15|iF 


Series inverter Resistance (R^) 


12Q 



VI.CONCLUSIONS 

In this proposed Unified Power Quality Conditioner 
(UPQC) is designed and simulated through synchronous 
reference frame theory with PI and Fuzzy logic controller. 
Simulation results show the proposed system's ability in 
voltage distortion, reactive power and current harmonics 
compensation. Fuzzy logic controller balances the power 
between series and shunt inverters by stabilizing DC link 
voltage in faster response as compare with PI controller. 

The operation of proposed system is analyzed using 
MATLAB/SIMULINK software. Simulation results confirm the 
correct operation of the proposed system. 

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Mr.R. V.D.Rama Rao received A.M.I.E graduation 
from I.E(India), Calcutta, India. The M.Tech Degree 
from J.N.T.U, Ananthapur.(with specialization in 
Power and Industrial Drives) in 1997and 2005 
respectively. He is presently working as an Associate Professor 
and HOD, EEE in Narasaraopeta Engineering College, 
Narasaraopet, India and pursuing part-time Ph.D in J.N.T.U, 
Hyderabad, Andhra Pradesh. His areas of interests include Power 
Quality by FACTS Controllers, controllers like Conventional 
controllers, Fuzzy controllers, Neuro Controller Neuro- Fuzzy 
controllers, Power Electronics and Drives. 

aDr.S.S.Dash received A.M.I.E graduation from I.E 
(India), Calcutta, India. The M.E Degree from 
U.C.E, Burla, Orissa, India,. (with specialization in 
Power Systems) and the Ph.D degree in Electrical 
-^^Engineering from Anna University College of 
B ^^BM Engineering, Guindy, Chennai-25 in 1994, 1996 and 
2006 respectively. He has published more number of Papers in 
National and International reputed Journals. He is presently 
working as Professor and HOD, EEE in SRM Engineering College, 
SRM University, Chennai, India and His areas of interests include 
FACTS, Power System operation, Control & Stability, Power 
Electronics & Drives and Intelligent controlling Techniques. 



©2011 ACEEE 

DOI: 01.IJEPE.02.01.78 



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